test/inference time augmentation

test.py

@@ -73,7 +73,7 @@ def test(cfg,
     for batch_i, (imgs, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
         imgs = imgs.to(device).float() / 255.0  # uint8 to float32, 0 - 255 to 0.0 - 1.0
         targets = targets.to(device)
-        _, _, height, width = imgs.shape  # batch size, channels, height, width
+        nb, _, height, width = imgs.shape  # batch size, channels, height, width
         whwh = torch.Tensor([width, height, width, height]).to(device)
 
         # Plot images with bounding boxes
@@ -83,11 +83,24 @@ def test(cfg,
 
         # Disable gradients
         with torch.no_grad():
+            aug = False  # augment https://github.com/ultralytics/yolov3/issues/931
+            if aug:
+                imgs = torch.cat((imgs,
+                                  imgs.flip(3),  # flip-lr
+                                  torch_utils.scale_img(imgs, 0.7),  # scale
+                                  ), 0)
+
             # Run model
             t = torch_utils.time_synchronized()
             inf_out, train_out = model(imgs)  # inference and training outputs
             t0 += torch_utils.time_synchronized() - t
 
+            if aug:
+                x = torch.split(inf_out, nb, dim=0)
+                x[1][..., 0] = width - x[1][..., 0]  # flip lr
+                x[2][..., :4] /= 0.7  # scale
+                inf_out = torch.cat(x, 1)
+
             # Compute loss
             if hasattr(model, 'hyp'):  # if model has loss hyperparameters
                 loss += compute_loss(train_out, targets, model)[1][:3]  # GIoU, obj, cls

utils/torch_utils.py

@@ -5,6 +5,7 @@ from copy import deepcopy
 import torch
 import torch.backends.cudnn as cudnn
 import torch.nn as nn
+import torch.nn.functional as F
 
 
 def init_seeds(seed=0):
@@ -105,6 +106,16 @@ def load_classifier(name='resnet101', n=2):
     return model
 
 
+def scale_img(img, r=1.0):  # img(16,3,256,416), r=ratio
+    # scales a batch of pytorch images while retaining same input shape (cropped or grey-padded)
+    h, w = img.shape[2:]
+    s = (int(h * r), int(w * r))  # new size
+    p = h - s[0], w - s[1]  # pad/crop pixels
+    img = F.interpolate(img, size=s, mode='bilinear', align_corners=False)  # resize
+    return F.pad(img, [0, p[1], 0, p[0]], value=0.5) if r < 1.0 else img[:, :, :p[0], :p[1]]  # pad/crop
+    # cv2.imwrite('scaled.jpg', np.array(img[0].permute((1, 2, 0)) * 255.0))
+
+
 class ModelEMA:
     """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models
     Keep a moving average of everything in the model state_dict (parameters and buffers).